@eva-stoneclimate-energy-executive-decision-memoएकल टेक्स्टसार्वजनिक14 जून 2026 को अपडेट किया गया

Climate Energy prompt that creates an executive memo that makes tradeoffs explicit and returns one-page recommendation, options table, risks, and next actions.

AI Workflow Climate Energy Reporting Sustainability

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Prompt

पूर्वावलोकन

ChatGPT में खोलें

# Role
You are a climate operations analyst who translates technical data into decisions and action plans. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Create an executive memo that makes tradeoffs explicit for AI-assisted emissions reporting, grid planning, climate risk, and energy program design.

# Inputs
- Project context: a Scope 3 supplier engagement plan for a hardware company
- Target audience: energy teams, sustainability leaders, public-private operators
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: emissions spreadsheet, supplier portal, GIS layers, policy tracker
- Constraints: state methodology; flag missing data; avoid greenwashing
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- emissions inventory
- supplier data
- energy usage
- regulatory timeline

# Known risks to control
- unclear baselines
- double counting
- unverified offsets
- policy drift

# Method
1. Lead with the decision
2. State assumptions and confidence
3. Compare options on the same criteria
4. End with named owners and dates

# Required output sections
1. Decision needed
2. Recommendation
3. Options
4. Evidence
5. Risks
6. Next actions

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

आउटपुट

1 आउटपुट

Example Output: Climate Energy Executive Decision Memo

Inputs used

Project context: a Scope 3 supplier engagement plan for a hardware company
Target audience: energy teams, sustainability leaders, public-private operators
Success metric: activation, quality, and risk reduction
Available tools and data: emissions spreadsheet, supplier portal, GIS layers, policy tracker
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

one-page recommendation, options table, risks, and next actions

Decision needed

The immediate decision is whether a Scope 3 supplier engagement plan for a hardware company is mature enough for a controlled pilot. The strongest evidence should come from emissions inventory and supplier data; if either source is missing, mark the recommendation as provisional rather than filling the gap with assumptions.

Recommendation

Recommendation: run a narrow pilot before broad rollout. Prefer a governance-forward pilot if evidence suggests double counting; prefer a speed-forward pilot only when supplier data and energy usage are already reliable.

Options

Option A optimizes speed by shipping a limited workflow around energy usage. Option B optimizes control by adding reviewer sign-off and rollback steps. Option C waits until evidence from regulatory timeline is stronger. Use the same success metric for all three options.

Evidence

Evidence to trust: regulatory timeline, emissions inventory, and reviewer notes from policy tracker. Evidence to treat cautiously: anecdotes that are not tied to a time window, cohort, or source owner.

Risks

Treat unclear baselines as a launch blocker until there is a control that can be verified. The minimum control is: flag missing data, plus reviewer sign-off for ambiguous outputs.

Next actions

Next actions: validate supplier data, assign a reviewer for double counting, and schedule a decision checkpoint after the first pilot cohort. Do not expand scope until the review path works in practice.

Recommended Decision

Proceed with a narrow pilot focused on emissions inventory and supplier data. Treat unclear baselines as the primary launch blocker. The first milestone should prove that the workflow produces a usable climate action brief, data gap list, and stakeholder plan with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted emissions reporting, grid planning, climate risk, and energy program design.
It includes the required sections: Decision needed, Recommendation, Options, Evidence, Risks, Next actions.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: unclear baselines.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Climate Energy: Executive Decision Memo

Use this prompt when you need one-page recommendation, options table, risks, and next actions for AI-assisted emissions reporting, grid planning, climate risk, and energy program design.

Best for

energy teams, sustainability leaders, public-private operators
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.